Semantic Context Research Articles

Global and Local Interactive Perception Network for Referring Image Segmentation.

The effective modal fusion and perception between the language and the image are necessary for inferring the reference instance in the referring image segmentation (RIS) task. In this article, we propose a novel RIS network, the global and local interactive perception network (GLIPN), to enhance the quality of modal fusion between the language and the image from the local and global perspectives. The core of GLIPN is the global and local interactive perception (GLIP) scheme. Specifically, the GLIP scheme contains the local perception module (LPM) and the global perception module (GPM). The LPM is designed to enhance the local modal fusion by the correspondence between word and image local semantics. The GPM is designed to inject the global structured semantics of images into the modal fusion process, which can better guide the word embedding to perceive the whole image's global structure. Combined with the local-global context semantics fusion, extensive experiments on several benchmark datasets demonstrate the advantage of the proposed GLIPN over most state-of-the-art approaches.

Sentiment analysis method of consumer comment text based on BERT and hierarchical attention in e-commerce big data environment

Abstract This study proposes an emotional analysis method of consumer comment text based on Bidirectional Encoder Representations from Transformers (BERT) and hierarchical attention. First, using the BERT pre-training model, the left and right contextual information is fused to enhance the semantic representation of words and generate dynamic word vectors containing contextual semantics. Second, the bidirectional long short-term memory network is used to obtain the sequence feature matrix, and the sentence representation and the text representation are obtained using the two-layer long short-term memory. Finally, the local attention mechanism and the global attention mechanism are introduced into the sentence representation layer and the text representation layer, respectively, and the text emotion of consumer comments is classified by softmax. Experiments show that the accuracy of the proposed method in Laptop data set is 93.01% and that in Restaurant data set is 92.45%. Therefore, the performance of the proposed method in the emotional analysis of consumer comment text is significantly better than that of the comparison method.

SEMANTICS OF PROVERBS ASSOCIATED WITH THE ZOONYM «WOLF» (BASED ON THE KAZAKH AND TURKISH LANGUAGES)

Comparative analysis of zoonyms, primarily the description of the national language picture of the world, is of undoubted scientific interest. After all, the comparison of any languages ​​serves as the basis for identifying three main qualities such as commonness, similarity and difference. The article analyzes one of the most ancient images of the Kazakh and Turkish culture - the zoosymbol "wolf" through proverbs. Proverbs are a kind of oral literature with the accumulated life experience of the people. They help to understand better the national identity, interests, attitudes, lifestyle and traditions of the people who created these proverbs. The study of this type of folklore gives a deeper understanding of the characteristics and relationship of different cultures.Proverbs about animals make up a large layer in the paremiological fund of different peoples and are distinguished by great thematic diversity. The main task of the entire structure of proverbs as a whole is to show the thoughts of the people in a complete, concise and clear form. Proverbs about animals are an example of archaic brevity and figurativeness. On the basis of proverbs about wolves in the Kazakh and Turkish languages, the ethno-cultural content of the zoonym "wolf" is determined, and a comparative analysis characterizes the complete preservation of the common Turkic line in the worldview of the two peoples. The article analyzes the contextual semantics of the Kazakh zoonym "wolf" and its Turkish equivalents in proverbs. The purpose of the study is to determine the features of the linguistic representation of the concept "wolf" in the Kazakh and Turkish languages ​​by means of a comparative analysis. As a result of the analysis, metaphorical meanings with positive and negative connotations were revealed. The semantic similarities and differences of proverbs associated with the zoonym "wolf" in the Kazakh and Turkish languages ​​are considered.

Information and Documentation Perspective to Intangible Cultural Heritage in the Context of Digital Humanities Research

This paper presents the critical role of digital humanities methods in safeguarding, research, and revitalisation of intangible cultural heritage. The study explores digitisation methods through a documentation and data management perspective, advocating for data standards that facilitate data interoperability at structure, content, value, and encoding levels. This paper critically analyses existing data standards and conceptual reference models, considering their potential and limitations for documenting and representing intangible cultural heritage. The typology of entities and processes related to intangible cultural heritage is presented and elaborated. Mapping identified entities and processes to individual classes in the CIDOC-CRM model is discussed to support the publication of data and semantic interoperability in the Semantic Web context.

CGT-YOLOv5n: A Precision Model for Detecting Mouse Holes Amid Complex Grassland Terrains

This study employs unmanned aerial vehicles (UAVs) to detect mouse holes in grasslands, offering an effective tool for grassland ecological conservation. We introduce the specially designed CGT-YOLOv5n model, addressing long-standing challenges UAVs face, particularly the decreased detection accuracy in complex grassland environments due to shadows and obstructions. The model incorporates a Context Augmentation Module (CAM) focused on improving the detection of small mouse holes and mitigating the interference of shadows. Additionally, to enhance the model’s ability to recognize mouse holes of varied morphologies, we have integrated an omni-dimensional dynamic convolution (ODConv), thereby increasing the model’s adaptability to diverse image features. Furthermore, the model includes a Task-Specific Context Decoupling (TSCODE) module, independently refining the contextual semantics and spatial details for classification and regression tasks and significantly improving the detection accuracy. The empirical results show that when the intersection over union (IoU) threshold is set at 0.5, the model’s mean average precision (mAP_0.5) for detection accuracy reaches 92.8%. The mean average precision (mAP_0.5:0.95), calculated over different IoU thresholds ranging from 0.5 to 0.95 in increments of 0.05, is 46.2%. These represent improvements of 3.3% and 4.3%, respectively, compared to the original model. Thus, this model contributes significantly to grassland ecological conservation and provides an effective tool for grassland management and mouse pest control in pastoral areas.

Semantic Technologies to Model Battery Data and Knowledge

Data-intensive battery research is here to stay. With open data incentives on the rise and simpler software interfaces granting access to powerful machine learning algorithms, researchers are uncovering a wealth of insights from battery data. However, data without context are just unusable digital bits. Battery data must be appropriately described to render it interoperable and reusable for traditional and data-intensive research. This requires overcoming numerous challenges associated to data heterogeneity, inconsistency, standardization, and meaning. Semantic technologies address these challenges by providing a unified and flexible framework for integrating and reasoning about data from heterogeneous sources.In this contribution, we will introduce Semantic Technologies and how they enable both human and software agents to understand and reuse battery data. We will present the tools we are developing to i) encourage the adoption of a common standard vocabulary to describe the battery domain[1], ii) connect research resources (datasets, experts, reports, publications, projects) within an accessible platform[2], and iii) reduce the effort required to upgrade data with semantic context. Overall, these tools take concrete steps towards guaranteeing compliance with FAIR data principles. We will further outline plans for future development, including strategies for reaching out to the global battery landscape, facilitate the curation of battery data for data-driven research, and enable knowledge exploration via natural language models.[1] S. Clark et al., "Toward a Unified Description of Battery Data," Adv. Energy Mater., vol. 2102702, 2021.[2] https://github.com/BIG-MAP/Demo-BatteryDataSemanticSearch

Kūnas krikščionybėje remiantis kūniškumo krize šiuolaikinėje kultūroje

Relevance of the topic. The article is devoted to the meanings and practises of the body in Christian culture and the issue of preserving and continuing this tradition in contemporary culture. Since it is about the semantic contexts of the body, the article uses the phenomenological method in combination with hermeneutic analysis. The authors consistently deploy several logical lines. The first line is related to the description of such a direction as hesychasm, its practises and attitude to the body. At the same time, the ethical potential of hesychasm and the pathos of physicality as an instrument of morality are emphasised. The second line revolves around the practical attitude to the body in modern culture: the preservation of the Christian tradition, the natural characteristics of the body, and understanding the meaning of the body. The third line is connected with the loss of the “sanctity” of the body and the unawareness of this loss on the part of the common citizen and, as a result, the erosion of the ethical and natural meaning of the body. According to the authors, history will show how terrible these changes in meaning can be. The “distortion” of the meaning of the body in modern cultures makes it possible to emphasise the emergence of the phenomenon of unconsciousness of paradoxical events, the enormity of the consequences of which few people still understand. The problem. Today, the events that unfold around the topic of the corporeal directly and indirectly touch on the topic of human relations and, as a result, there is a need to once again talk about the role and significance of the body in the Christian tradition and about the consequences of ignoring the biblical commandments in modern everyday life. The purpose. To clarify the meanings of the corporeal, based on the tradition of hesychasm. Research object. The role and significance of the body in the Christian tradition and about the consequences of ignoring the biblical commandments in modern everyday life. Results. In the article, it was shown that the body and its meaning have a special ethical pathos because its understanding was formed under the enormous influence of Christianity, which laid the foundations for the formation of the context of the general civilisational development of mankind. Modern culture “distorts” the meaning of the body, and the actual material of sports competitions makes it possible to record the phenomenon of unawareness in most people that there is a departure from universal values (this is what is called a crisis of culture). History moves in such a way that sometimes it is impossible to trace the trajectories of its development. And yet, despite the incomprehensible lines of development, does the question of the relationship with the Absolute remain relevant for a man? If the Christian meanings of the body fade into the background of life, then what other religion will be able to promote the ideas of ethics and morality? Keywords. Hesychasm, Christian tradition, meanings of the body, transgender, sports competitions, the pathos of the body, contemporary culture.

Learning from User-driven Events to Generate Automation Sequences

Enabling smart devices to learn automating actions as expected is a crucial yet challenging task. The traditional Trigger-Action rule approach for device automation is prone to ambiguity in complex scenarios. To address this issue, we propose a data-driven approach that leverages recorded user-driven event sequences to predict potential actions users may take and generate fine-grained device automation sequences. Our key intuition is that user-driven event sequences, like human-written articles and programs, are governed by consistent semantic contexts and contain regularities that can be modeled to generate sequences that express the user's preferences. We introduce ASGen, a deep learning framework that combines sequential information, event attributes, and external knowledge to form the event representation and output sequences of arbitrary length to facilitate automation. To evaluate our approach from both quantitative and qualitative perspectives, we conduct two studies using a realistic dataset containing over 4.4 million events. Our results show that our approach surpasses other methods by providing more accurate recommendations. And the automation sequences generated by our model are perceived as equally or even more rational and useful compared to those generated by humans.

Multi-modal semantics fusion model for domain relation extraction via information bottleneck

Domain relation extraction (DRE) aims to understand domain semantics of unstructured text to mine structural knowledge. However, most existing DRE models may fail to fully represent the domain entity that is usually absent in the corpus of pre-trained language models. In addition, almost all these models directly use contextual semantics for relation classification that ignore purifying the task relevant features. To address these challenging problems, we propose a novel and effective Multi-Modal Semantics Fusion (MMSF) model, which can automatically capture domain semantics to discover the important semantics contained in images, thus enhancing the representations of the domain entity by exploiting the multi-modal semantics. Afterwards, we introduce heterogeneous graph neural networks to learn contextual semantics. To obtain the task relevant features for DRE, we design a new purified feature architecture by exploring the mutual information between contextual semantics and the labels of domain relation. With the enhanced representations of the domain entity and purified the task relevant features, the satisfactory results of relation classification can be guaranteed. Extensive experiments on two real domain relation extraction datasets demonstrate the superiority of our proposed model over several state-of-the-art models. The source code and experiment details of this paper can be obtained from https://github.com/SWT-AITeam/MMSF.

Effect of contextual diversity on word recognition in different semantic contexts.

Efficient word recognition is important to facilitate reading comprehension. Two important factors influence word recognition-word frequency (WF) and contextual diversity (CD)-but studies have not reached consistent conclusions on their role. Based on previous studies, the present study strictly controlled the anticipation of sentence context on target words. In the context of the semantic incongruence of Chinese sentences-that is, when the context is equivalent and low in anticipation of the target noun-CD effects were found on late processing indicators of the eye movement data of parafoveal words, and the CD feature of parafoveal words led to a significant parafoveal-on-foveal effect. However, none of these results were found in the semantically reasonable (semantic congruence) context. The results suggested that high CD words are better at adapting to unexposed or learned contexts, which was not the case for high WF words.

Fusion of SoftLexicon and RoBERTa for Purpose-Driven Electronic Medical Record Named Entity Recognition

Recently, researchers have extensively explored various methods for electronic medical record named entity recognition, including character-based, word-based, and hybrid methods. Nonetheless, these methods frequently disregard the semantic context of entities within electronic medical records, leading to the creation of subpar-quality clinical knowledge bases and obstructing the discovery of clinical knowledge. In response to these challenges, we propose a novel purpose-driven SoftLexicon-RoBERTa-BiLSTM-CRF (SLRBC) model for electronic medical records named entity recognition. SLRBC leverages the fusion of SoftLexicon and RoBERTa to incorporate the word lexicon information from electronic medical records into the character representations, enhancing the model’s semantic embedding representations. This purpose-driven approach helps achieve a more comprehensive representation and avoid common segmentation errors, consequently boosting the accuracy of entity recognition. Furthermore, we employ the classical BiLSTM-CRF framework to capture contextual information of entities more effectively. In order to assess the performance of SLRBC, a series of experiments on the public datasets of CCKS2018 and CCKS2019 were conducted. The experimental results demonstrate that SLRBC can efficiently extract entities from Chinese electronic medical records. The model attains F1 scores of 94.97% and 85.40% on CCKS2018 and CCKS2019, respectively, exhibiting outstanding performance in the extraction and utilization efficiency of clinical information.

EPAG: A novel enhanced move recognition algorithm based on continuous learning mechanism with positional embedding

The identification of abstracts plays a vital role in efficiently locating the content and providing clarity to the article. Existing algorithms for move recognition exhibit a deficiency in their capacity to acquire word adjacent position information when word changes in Chinese expressions to obtain contextual semantics changes. This paper introduces EPAG: a novel enhanced move recognition algorithm with the improved pre-trained framework and downstream model for unstructured abstracts of Chinese scientific and technological papers. The proposed algorithm first performs data segmentation and vocabulary training. The EPAG framework is leveraged to incorporate word positional information, facilitating deep semantic learning and targeted feature extraction. Experimental results demonstrate that the proposed algorithm achieves 13.37% higher accuracy on the split dataset than on the original dataset and a 7.55% improvement in accuracy over the basic comparison model.

IDP-Net: Industrial defect perception network based on cross-layer semantic information guidance and context concentration enhancement

Applications in Engineering: In industry, surface defect detection is crucial for improving product quality. However, there are many challenges in industrial inspection scenarios, such as interference from background noise, complex small-target problems, significant variations in target objects, and the problem of finding a balance between inspection speed and accuracy. To address the above problems, this paper proposes an industrial defect-aware network based on cross-layer semantic information guidance and contextual attention enhancement (IDP-Net). Specifically, IDP-Net has four different new features. The contribution of artificial intelligence: Firstly, to solve the industrial surface context and defect similarity problem, this paper proposes a Lightweight Local Global Feature Extraction Network (LLG-Net), unlike other methods, the effective combination of self-attention blocks and convolution blocks ensures gradual integration of global and local features across multiple layers, to improve the detection ability of targets with significant changes in scale, this paper designs a Multiscale Perceptual Feature Aggregation Network (MPA-Net), adequately fuses the shallow fine-grained information and the deep semantic information. Then, to enhance the connection between multi-scale semantic information, an adaptive cross-layer feature fusion module (ACFF) is proposed, which is novel in integrating the characteristics of multiple adjacent levels to help the model better capture the different scale characterisation of the target. Finally, a Region Attention Module (RAM) is proposed and introduced in the detector to enhance the attention to the critical regions around the target object. In particular, this paper proposes a new localisation loss function (MEIoU) that enhances the network’s attention to objects at different scales. The experimental results show that 94.3%, 98.7% and 99.5% of mAP@.5 are obtained on steel, PCB and aluminium surface defect datasets, respectively, and 50 FPS is achieved, which is better than the current mainstream detectors and meets the demand of practical industrial production.

A combination network of CNN and transformer for interference identification.

Communication interference identification is critical in electronic countermeasures. However, existed methods based on deep learning, such as convolutional neural networks (CNNs) and transformer, seldom take both local characteristics and global feature information of the signal into account. Motivated by the local convolution property of CNNs and the attention mechanism of transformer, we designed a novel network that combines both architectures, which make better use of both local and global characteristics of the signals. Additionally, recognizing the challenge of distinguishing contextual semantics within the one-dimensional signal data used in this study, we advocate the use of CNNs in place of word embedding, aligning more closely with the intrinsic features of the signal data. Furthermore, to capture the time-frequency characteristics of the signals, we integrate the proposed network with a cross-attention mechanism, facilitating the fusion of temporal and spectral domain feature information through multiple cross-attention computational layers. This innovation obviates the need for specialized time-frequency analysis. Experimental results demonstrate that our approach significantly improves recognition accuracy compared to existing methods, highlighting its efficacy in addressing the challenge of communication interference identification in electronic warfare.

Enhancing gastrointestinal submucosal tumor recognition in endoscopic ultrasonography: A novel multi-attribute guided contextual attention network

Endoscopic ultrasonography (EUS) is a valuable imaging modality for diagnosing gastrointestinal submucosal tumors (SMTs). However, inherent content variations in EUS images due to gastrointestinal tract mobility and handheld ultrasound instability challenge the saliency of SMTs’ visual features. The presence of fine-grained inter-class and large intra-class differences further complicates EUS-based diagnosis. To address these issues, this paper presents a novel Multi-Attribute Guided Contextual Attention Network (MAG-CA-Net) for interpretable SMT recognition in EUS. Inspired by endoscopists’ clinical diagnosis expertise, our framework initially localizes abnormal areas based on echo attributes and subsequently determines tumor categories using contextual semantics. Experimental results demonstrate the effectiveness of MAG-CA-Net, exhibiting improved recognition recall and precision rates for gastrointestinal stromal tumor, leiomyoma, and pancreatic rest. Specifically, the MAG network facilitates abnormal area localization, while the CA mechanism guides the model to focus on the most discriminative tumor-context-associated regions. The proposed method achieved an average classification accuracy of 93.16%, an average precision of 93.17%, a weighted recall of 93.16%, and an average F1-score of 93.15 % for the three disease categories. The proposed approach provides crucial guidelines for data collection standards and model development in the clinical diagnosis process of SMTs under EUS. Its interpretability analysis enhances the credibility of clinical physicians towards assisted diagnostic methods based on deep learning. The source code will be publicly available at https://gitee.com/HangbinZheng/mag-ca-net.

Pragmatic determination ofas if(akoby)in ego-related contexts

AbstractThe impetus to write this paper was an analysis of the expressionas if(akoby) as a component of the “equivalence” principle and the determination of its legitimacy as a component of the “behavior” principle. The study is focused on the search for further connections between the verbal behavior of the speaker and the linguistic aspects ofas if(akoby) because it is used as a manifestation of motivated, ego-related and assertive verbal behavior.As if(akoby) can be viewed as: (a) a component of the principle of verbal behavior that attributes an egocentric meaning to the speaker’s behavior; (b) an egocentric expression of the hypotaxis with an implicit speaker and the semantics of circumstantial and characteristic contexts; (c) a pragmatic marker of the speaker’s ego-related interpretation of facts. Interpretation, which is grasped at the following two levels, is the methodological framework of the monitored aspects: (i) in the sense of the ego-related evaluation of a fact or event by the speaker, i.e. in the sense of participatory interpretation, and (ii) in the sense of explaining the verbal behavior of the speaker, i.e. in the sense of searching for the motives, intentions and goals that form the preconditions for his/her verbal behavior.

Optimality Theory Paradigm in the Formation of Active Participles Arabic Quadrilateral Verbs

This article aims to investigate the implementation of the Optimality Theory (OT) paradigm in the formation of Active Participle (AP) in quadrilateral verbs in the Arabic language. This approach involves morphological and contrastive linguistic analyses to understand the patterns of PA formation from quadrilateral verbs, with a focus on the hierarchy of criteria and candidate evaluation based on OT principles. The research methodology includes an in-depth study of various forms of PA produced by quadrilateral verbs, identifying rules and preferences that lead to changes in the word forms. The research findings indicate that OT provides profound insights into the complexity of the morphology of quadrilateral verbs, illustrating consistent patterns of change as well as variations depending on grammatical and semantic contexts. The implications of these research findings significantly contribute to the understanding of Arabic linguistic structure, morphological theory, and contrastive analysis methods. By paving the way for further research, this article suggests that paradigm serves as an effective conceptual framework to explain morphological phenomena in the Arabic language, enriching Arabic linguistic literature and supporting the development of advanced morphological analysis methods. This study opens avenues for deeper exploration of linguistic intricacies, fostering academic insights into the Arabic language's structural nuances.

Leveraging data-driven and procedural methods for generating high-fidelity visualizations of real forests

Forests are indispensable in mitigating climate change due to their ability to sequester and store large amounts of carbon. Forest restoration and maintenance activities are crucial for retaining and enhancing this capacity. To promote sustainable practices, stakeholders need to assess the potential impacts of forestry operations. But in-depth comprehension of the forest dynamics is challenging due to the complex interactions between various ecosystem components. High-fidelity, immersive visualizations can support sense- and decision-making processes by facilitating the exploration of scientific and non-scientific data in the respective spatial, temporal, and semantic contexts. However, producing accurate representations of diverse ecosystems is complex and time-consuming. In this paper, we introduce a forest visualization prototype providing realistic representations of forest stands and different simulated scenarios. The utilized data-driven and procedural approach ensures efficiency, reproducibility, and accuracy. Furthermore, we present an array of requirements for realistic forest visualizations gathered through questionnaires and interviews with forestry experts.

BlockMatch: A Fine-Grained Binary Code Similarity Detection Approach Using Contrastive Learning for Basic Block Matching

Binary code similarity detection (BCSD) plays a vital role in computer security and software engineering. Traditional BCSD methods heavily rely on specific features and necessitate rich expert knowledge, which are sensitive to code alterations. To improve the robustness against minor code alterations, recent research has shifted towards machine learning-based approaches. However, existing BCSD approaches mainly focus on function-level matching and face challenges related to large batch optimization and high quality sample selection at the basic block level. To overcome these challenges, we propose BlockMatch, a novel fine-grained BCSD approach that leverages natural language processing (NLP) techniques and contrastive learning for basic block matching. We treat instructions of basic blocks as a language and utilize a DeBERTa model to capture relative position relations and contextual semantics for encoding instruction sequences. For various operands in binary code, we propose a root operand model pre-training task to mitigate semantic missing of unseen operands. We then employ a mean pooling layer to generate basic block embeddings for detecting binary code similarity. Additionally, we propose a contrastive training framework, including a block augmentation model to generate high-quality training samples, improving the effectiveness of model training. Inspired by contrastive learning, we adopt the NT-Xent loss as our objective function, which allows larger sample sizes for model training and mitigates the convergence issues caused by limited local positive/negative samples. By conducting extensive experiments, we evaluate BlockMatch and compare it against state-of-the-art approaches such as PalmTree and SAFE. The results demonstrate that BlockMatch achieves a recall@1 of 0.912 at the basic block level under the cross-compiler scenario (pool size = 10), which outperforms PalmTree (0.810) and SAFE (0.798). Furthermore, our ablation study shows that the proposed contrastive training framework and root operand model pre-training task help our model achieve superior performance.

MVSSC: Meta-reinforcement learning based visual indoor navigation using multi-view semantic spatial context

In Visual Indoor Navigation (VIN), Deep Reinforcement Learning (DRL) is commonly used by agents to achieve end-to-end mapping from vision to action when navigating toward a target based on observation. However, current DRL-based work suffers from two challenges: partial observability resulting from using solely a single first-person view and poor generalization in the case of unknown scenes and unknown objects. In light of these issues, this paper introduces the integration of multi-view as an expansion of observability and meta-learning as a primary generalization technique into the DRL framework and presents the meta-reinforcement learning method that leverages Multi-View Semantic Spatial Context (MVSSC). Specifically, aiming to explore the informative multi-view context for better efficiency in searching for and navigating to the target, we model the objects’ relationship from two aspects, multi-view semantic context (MVSEC) and multi-view spatial context (MVSPC). MVSEC enables agents to encode prior semantic relationships adaptively via a multi-view modulated graph. Meanwhile, MVSPC enhances the spatial representation of target-related objects’ correlation through similarity grids of multi-view. After adaptively fusing the multi-view and context information under the meta-reinforcement learning framework, our method can encourage efficient target search and robust navigation with stronger generalization performance to unknown scenes and unknown objects. Extensive experimental results on the AI2-THOR simulator demonstrate that our method outperforms the current state-of-the-art approaches.